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@article{205652,
        author = {A.Shanmuganaadhan and Dr.P.Sivasankar},
        title = {Cloudburst Identification and Forecasting System – Powered by Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7555-7563},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205652},
        abstract = {Cloudburst is a sudden rainfall event occurring over a small area in a shorter duration, typically the rainfall may be exceeding the range of 100 mm in a hour which causes flash flood and severe landslides on the affected area. Predicting this type of event accurately in time is essential for the world to minimize their impact created after when the event occurred and also to save lives. This project introduces a Cloudburst Identification and Forecasting System which is designed to monitor and analyze the real-time weather data from Open-Meteo API and NASA POWER API predicts the result with the Machine Learning model called Random Forest Classifier in which it takes five key parameters such as Temperature, Humidity, Wind Speed, Presssure, Rainfall. By applying the machine learning techniques, the system will predict the probability of a cloudburst for the next 30 minutes from current time by analyzing and processing with the current Real-time weather parameters using the Random Forest Classifier which is been trained with 50,000 datasets. It also delivers early alerts and detailed visual insights in the dashboard to support fast and quick decision making for both Government Authorities and for the common people to react quickly and to reduce the risk of affecting by the event. With our system we focus mainly on the Cloudburst events to strengthen the disaster preparedness in the high-risk areas to reduce the risk and save lives associated with this sudden extreme weather disaster event.},
        keywords = {Cloudburst, Identification and Forecasting, Open-Meteo API, NASA POWER API, Random Forest Model, Real-time weather parameters.},
        month = {June},
        }